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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neighbors</span></code>.KNeighborsRegressor</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-neighbors-kneighborsregressor">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.neighbors.KNeighborsRegressor</span></code></a></li>
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  <div class="section" id="sklearn-neighbors-kneighborsregressor">
<h1><a class="reference internal" href="../classes.html#module-sklearn.neighbors" title="sklearn.neighbors"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neighbors</span></code></a>.KNeighborsRegressor<a class="headerlink" href="#sklearn-neighbors-kneighborsregressor" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.neighbors.KNeighborsRegressor">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.neighbors.</code><code class="sig-name descname">KNeighborsRegressor</code><span class="sig-paren">(</span><em class="sig-param">n_neighbors=5</em>, <em class="sig-param">weights='uniform'</em>, <em class="sig-param">algorithm='auto'</em>, <em class="sig-param">leaf_size=30</em>, <em class="sig-param">p=2</em>, <em class="sig-param">metric='minkowski'</em>, <em class="sig-param">metric_params=None</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_regression.py#L23"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor" title="Permalink to this definition">¶</a></dt>
<dd><p>Regression based on k-nearest neighbors.</p>
<p>The target is predicted by local interpolation of the targets
associated of the nearest neighbors in the training set.</p>
<p>Read more in the <a class="reference internal" href="../neighbors.html#regression"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.9.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>n_neighbors</strong><span class="classifier">int, optional (default = 5)</span></dt><dd><p>Number of neighbors to use by default for <a class="reference internal" href="#sklearn.neighbors.KNeighborsRegressor.kneighbors" title="sklearn.neighbors.KNeighborsRegressor.kneighbors"><code class="xref py py-meth docutils literal notranslate"><span class="pre">kneighbors</span></code></a> queries.</p>
</dd>
<dt><strong>weights</strong><span class="classifier">str or callable</span></dt><dd><p>weight function used in prediction.  Possible values:</p>
<ul class="simple">
<li><p>‘uniform’ : uniform weights.  All points in each neighborhood
are weighted equally.</p></li>
<li><p>‘distance’ : weight points by the inverse of their distance.
in this case, closer neighbors of a query point will have a
greater influence than neighbors which are further away.</p></li>
<li><p>[callable] : a user-defined function which accepts an
array of distances, and returns an array of the same shape
containing the weights.</p></li>
</ul>
<p>Uniform weights are used by default.</p>
</dd>
<dt><strong>algorithm</strong><span class="classifier">{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional</span></dt><dd><p>Algorithm used to compute the nearest neighbors:</p>
<ul class="simple">
<li><p>‘ball_tree’ will use <a class="reference internal" href="sklearn.neighbors.BallTree.html#sklearn.neighbors.BallTree" title="sklearn.neighbors.BallTree"><code class="xref py py-class docutils literal notranslate"><span class="pre">BallTree</span></code></a></p></li>
<li><p>‘kd_tree’ will use <a class="reference internal" href="sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree" title="sklearn.neighbors.KDTree"><code class="xref py py-class docutils literal notranslate"><span class="pre">KDTree</span></code></a></p></li>
<li><p>‘brute’ will use a brute-force search.</p></li>
<li><p>‘auto’ will attempt to decide the most appropriate algorithm
based on the values passed to <a class="reference internal" href="#sklearn.neighbors.KNeighborsRegressor.fit" title="sklearn.neighbors.KNeighborsRegressor.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> method.</p></li>
</ul>
<p>Note: fitting on sparse input will override the setting of
this parameter, using brute force.</p>
</dd>
<dt><strong>leaf_size</strong><span class="classifier">int, optional (default = 30)</span></dt><dd><p>Leaf size passed to BallTree or KDTree.  This can affect the
speed of the construction and query, as well as the memory
required to store the tree.  The optimal value depends on the
nature of the problem.</p>
</dd>
<dt><strong>p</strong><span class="classifier">integer, optional (default = 2)</span></dt><dd><p>Power parameter for the Minkowski metric. When p = 1, this is
equivalent to using manhattan_distance (l1), and euclidean_distance
(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.</p>
</dd>
<dt><strong>metric</strong><span class="classifier">string or callable, default ‘minkowski’</span></dt><dd><p>the distance metric to use for the tree.  The default metric is
minkowski, and with p=2 is equivalent to the standard Euclidean
metric. See the documentation of the DistanceMetric class for a
list of available metrics.
If metric is “precomputed”, X is assumed to be a distance matrix and
must be square during fit. X may be a <a class="reference internal" href="../../glossary.html#term-sparse-graph"><span class="xref std std-term">Glossary</span></a>,
in which case only “nonzero” elements may be considered neighbors.</p>
</dd>
<dt><strong>metric_params</strong><span class="classifier">dict, optional (default = None)</span></dt><dd><p>Additional keyword arguments for the metric function.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int or None, optional (default=None)</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend" title="(in joblib v0.14.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n-jobs"><span class="xref std std-term">Glossary</span></a>
for more details.
Doesn’t affect <a class="reference internal" href="#sklearn.neighbors.KNeighborsRegressor.fit" title="sklearn.neighbors.KNeighborsRegressor.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> method.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>effective_metric_</strong><span class="classifier">string or callable</span></dt><dd><p>The distance metric to use. It will be same as the <code class="docutils literal notranslate"><span class="pre">metric</span></code> parameter
or a synonym of it, e.g. ‘euclidean’ if the <code class="docutils literal notranslate"><span class="pre">metric</span></code> parameter set to
‘minkowski’ and <code class="docutils literal notranslate"><span class="pre">p</span></code> parameter set to 2.</p>
</dd>
<dt><strong>effective_metric_params_</strong><span class="classifier">dict</span></dt><dd><p>Additional keyword arguments for the metric function. For most metrics
will be same with <code class="docutils literal notranslate"><span class="pre">metric_params</span></code> parameter, but may also contain the
<code class="docutils literal notranslate"><span class="pre">p</span></code> parameter value if the <code class="docutils literal notranslate"><span class="pre">effective_metric_</span></code> attribute is set to
‘minkowski’.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.neighbors.NearestNeighbors.html#sklearn.neighbors.NearestNeighbors" title="sklearn.neighbors.NearestNeighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NearestNeighbors</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.neighbors.RadiusNeighborsRegressor.html#sklearn.neighbors.RadiusNeighborsRegressor" title="sklearn.neighbors.RadiusNeighborsRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RadiusNeighborsRegressor</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier" title="sklearn.neighbors.KNeighborsClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KNeighborsClassifier</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.neighbors.RadiusNeighborsClassifier.html#sklearn.neighbors.RadiusNeighborsClassifier" title="sklearn.neighbors.RadiusNeighborsClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RadiusNeighborsClassifier</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>See <a class="reference internal" href="../neighbors.html#neighbors"><span class="std std-ref">Nearest Neighbors</span></a> in the online documentation
for a discussion of the choice of <code class="docutils literal notranslate"><span class="pre">algorithm</span></code> and <code class="docutils literal notranslate"><span class="pre">leaf_size</span></code>.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Regarding the Nearest Neighbors algorithms, if it is found that two
neighbors, neighbor <code class="docutils literal notranslate"><span class="pre">k+1</span></code> and <code class="docutils literal notranslate"><span class="pre">k</span></code>, have identical distances but
different labels, the results will depend on the ordering of the
training data.</p>
</div>
<p><a class="reference external" href="https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm">https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm</a></p>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsRegressor</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span> <span class="o">=</span> <span class="n">KNeighborsRegressor</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">KNeighborsRegressor(...)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">neigh</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mf">1.5</span><span class="p">]]))</span>
<span class="go">[0.5]</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsRegressor.fit" title="sklearn.neighbors.KNeighborsRegressor.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X, y)</p></td>
<td><p>Fit the model using X as training data and y as target values</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsRegressor.get_params" title="sklearn.neighbors.KNeighborsRegressor.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsRegressor.kneighbors" title="sklearn.neighbors.KNeighborsRegressor.kneighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kneighbors</span></code></a>(self[, X, n_neighbors, …])</p></td>
<td><p>Finds the K-neighbors of a point.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsRegressor.kneighbors_graph" title="sklearn.neighbors.KNeighborsRegressor.kneighbors_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kneighbors_graph</span></code></a>(self[, X, n_neighbors, mode])</p></td>
<td><p>Computes the (weighted) graph of k-Neighbors for points in X</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsRegressor.predict" title="sklearn.neighbors.KNeighborsRegressor.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(self, X)</p></td>
<td><p>Predict the target for the provided data</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsRegressor.score" title="sklearn.neighbors.KNeighborsRegressor.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(self, X, y[, sample_weight])</p></td>
<td><p>Return the coefficient of determination R^2 of the prediction.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsRegressor.set_params" title="sklearn.neighbors.KNeighborsRegressor.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.neighbors.KNeighborsRegressor.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">n_neighbors=5</em>, <em class="sig-param">weights='uniform'</em>, <em class="sig-param">algorithm='auto'</em>, <em class="sig-param">leaf_size=30</em>, <em class="sig-param">p=2</em>, <em class="sig-param">metric='minkowski'</em>, <em class="sig-param">metric_params=None</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_regression.py#L142"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsRegressor.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_base.py#L1096"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the model using X as training data and y as target values</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix, BallTree, KDTree}</span></dt><dd><p>Training data. If array or matrix, shape [n_samples, n_features],
or [n_samples, n_samples] if metric=’precomputed’.</p>
</dd>
<dt><strong>y</strong><span class="classifier">{array-like, sparse matrix}</span></dt><dd><dl class="simple">
<dt>Target values, array of float values, shape = [n_samples]</dt><dd><p>or [n_samples, n_outputs]</p>
</dd>
</dl>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsRegressor.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsRegressor.kneighbors">
<code class="sig-name descname">kneighbors</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X=None</em>, <em class="sig-param">n_neighbors=None</em>, <em class="sig-param">return_distance=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_base.py#L531"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.kneighbors" title="Permalink to this definition">¶</a></dt>
<dd><p>Finds the K-neighbors of a point.
Returns indices of and distances to the neighbors of each point.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_queries, n_features),                 or (n_queries, n_indexed) if metric == ‘precomputed’</span></dt><dd><p>The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.</p>
</dd>
<dt><strong>n_neighbors</strong><span class="classifier">int</span></dt><dd><p>Number of neighbors to get (default is the value
passed to the constructor).</p>
</dd>
<dt><strong>return_distance</strong><span class="classifier">boolean, optional. Defaults to True.</span></dt><dd><p>If False, distances will not be returned</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>neigh_dist</strong><span class="classifier">array, shape (n_queries, n_neighbors)</span></dt><dd><p>Array representing the lengths to points, only present if
return_distance=True</p>
</dd>
<dt><strong>neigh_ind</strong><span class="classifier">array, shape (n_queries, n_neighbors)</span></dt><dd><p>Indices of the nearest points in the population matrix.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<p>In the following example, we construct a NeighborsClassifier
class from an array representing our data set and ask who’s
the closest point to [1,1,1]</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">samples</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span> <span class="o">=</span> <span class="n">NearestNeighbors</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
<span class="go">NearestNeighbors(n_neighbors=1)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">neigh</span><span class="o">.</span><span class="n">kneighbors</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]))</span>
<span class="go">(array([[0.5]]), array([[2]]))</span>
</pre></div>
</div>
<p>As you can see, it returns [[0.5]], and [[2]], which means that the
element is at distance 0.5 and is the third element of samples
(indexes start at 0). You can also query for multiple points:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">kneighbors</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">return_distance</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="go">array([[1],</span>
<span class="go">       [2]]...)</span>
</pre></div>
</div>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsRegressor.kneighbors_graph">
<code class="sig-name descname">kneighbors_graph</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X=None</em>, <em class="sig-param">n_neighbors=None</em>, <em class="sig-param">mode='connectivity'</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_base.py#L705"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.kneighbors_graph" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the (weighted) graph of k-Neighbors for points in X</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_queries, n_features),                 or (n_queries, n_indexed) if metric == ‘precomputed’</span></dt><dd><p>The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.</p>
</dd>
<dt><strong>n_neighbors</strong><span class="classifier">int</span></dt><dd><p>Number of neighbors for each sample.
(default is value passed to the constructor).</p>
</dd>
<dt><strong>mode</strong><span class="classifier">{‘connectivity’, ‘distance’}, optional</span></dt><dd><p>Type of returned matrix: ‘connectivity’ will return the
connectivity matrix with ones and zeros, in ‘distance’ the
edges are Euclidean distance between points.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>A</strong><span class="classifier">sparse graph in CSR format, shape = [n_queries, n_samples_fit]</span></dt><dd><p>n_samples_fit is the number of samples in the fitted data
A[i, j] is assigned the weight of edge that connects i to j.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.neighbors.NearestNeighbors.html#sklearn.neighbors.NearestNeighbors.radius_neighbors_graph" title="sklearn.neighbors.NearestNeighbors.radius_neighbors_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NearestNeighbors.radius_neighbors_graph</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span> <span class="o">=</span> <span class="n">NearestNeighbors</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">NearestNeighbors(n_neighbors=2)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">A</span> <span class="o">=</span> <span class="n">neigh</span><span class="o">.</span><span class="n">kneighbors_graph</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">A</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
<span class="go">array([[1., 0., 1.],</span>
<span class="go">       [0., 1., 1.],</span>
<span class="go">       [1., 0., 1.]])</span>
</pre></div>
</div>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsRegressor.predict">
<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_regression.py#L158"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict the target for the provided data</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_queries, n_features),                 or (n_queries, n_indexed) if metric == ‘precomputed’</span></dt><dd><p>Test samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y</strong><span class="classifier">array of int, shape = [n_queries] or [n_queries, n_outputs]</span></dt><dd><p>Target values</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsRegressor.score">
<code class="sig-name descname">score</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L376"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the coefficient of determination R^2 of the prediction.</p>
<p>The coefficient R^2 is defined as (1 - u/v), where u is the residual
sum of squares ((y_true - y_pred) ** 2).sum() and v is the total
sum of squares ((y_true - y_true.mean()) ** 2).sum().
The best possible score is 1.0 and it can be negative (because the
model can be arbitrarily worse). A constant model that always
predicts the expected value of y, disregarding the input features,
would get a R^2 score of 0.0.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Test samples. For some estimators this may be a
precomputed kernel matrix or a list of generic objects instead,
shape = (n_samples, n_samples_fitted),
where n_samples_fitted is the number of
samples used in the fitting for the estimator.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True values for X.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>R^2 of self.predict(X) wrt. y.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>The R2 score used when calling <code class="docutils literal notranslate"><span class="pre">score</span></code> on a regressor will use
<code class="docutils literal notranslate"><span class="pre">multioutput='uniform_average'</span></code> from version 0.23 to keep consistent
with <a class="reference internal" href="sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">r2_score</span></code></a>. This will influence the
<code class="docutils literal notranslate"><span class="pre">score</span></code> method of all the multioutput regressors (except for
<a class="reference internal" href="sklearn.multioutput.MultiOutputRegressor.html#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiOutputRegressor</span></code></a>). To specify the
default value manually and avoid the warning, please either call
<a class="reference internal" href="sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">r2_score</span></code></a> directly or make a custom scorer with
<a class="reference internal" href="sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_scorer</span></code></a> (the built-in scorer <code class="docutils literal notranslate"><span class="pre">'r2'</span></code> uses
<code class="docutils literal notranslate"><span class="pre">multioutput='uniform_average'</span></code>).</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsRegressor.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-neighbors-kneighborsregressor">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.neighbors.KNeighborsRegressor</span></code><a class="headerlink" href="#examples-using-sklearn-neighbors-kneighborsregressor" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This example shows the use of multi-output estimator to complete images. The goal is to predict..."><div class="figure align-default" id="id1">
<img alt="../../_images/sphx_glr_plot_multioutput_face_completion_thumb.png" src="../../_images/sphx_glr_plot_multioutput_face_completion_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/plot_multioutput_face_completion.html#sphx-glr-auto-examples-plot-multioutput-face-completion-py"><span class="std std-ref">Face completion with a multi-output estimators</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The sklearn.impute.IterativeImputer class is very flexible - it can be used with a variety of e..."><div class="figure align-default" id="id2">
<img alt="../../_images/sphx_glr_plot_iterative_imputer_variants_comparison_thumb.png" src="../../_images/sphx_glr_plot_iterative_imputer_variants_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/impute/plot_iterative_imputer_variants_comparison.html#sphx-glr-auto-examples-impute-plot-iterative-imputer-variants-comparison-py"><span class="std std-ref">Imputing missing values with variants of IterativeImputer</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrate the resolution of a regression problem using a k-Nearest Neighbor and the interpola..."><div class="figure align-default" id="id3">
<img alt="../../_images/sphx_glr_plot_regression_thumb.png" src="../../_images/sphx_glr_plot_regression_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_regression.html#sphx-glr-auto-examples-neighbors-plot-regression-py"><span class="std std-ref">Nearest Neighbors regression</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
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